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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

Science China(Information Sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

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Graph for Science: From API based Programming to Graph Engine based Programming for HPC

arXiv

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arXiv 2023年

作者： Zhang, Yu Wang, Zixiao Zhao, Jin Guo, Yuluo Yu, Hui Huang, Zhiying Shi, Xuanhua Liao, Xiaofei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Modern scientific applications predominantly run on large-scale computing platforms, necessitating collab.ration between scientific domain experts and high-performance computing (HPC) experts. While domain experts are often skilled in customizing domain-specific scientific computing routines, which often involves various matrix computations, HPC experts are essential for achieving efficient execution of these computations on large-scale platforms. This process often involves utilizing complex parallel computing libraries tailored to specific matrix computation scenarios. However, the intricate programming procedure and the need for deep understanding in both application domains and HPC poses significant challenges to the widespread adoption of scientific computing. In this research, we observe that matrix computations can be transformed into equivalent graph representations, and that by utilizing graph processing engines, HPC experts can be freed from the burden of implementing efficient scientific computations. Based on this observation, we introduce a graph engine-based scientific computing (Graph for Science) paradigm, which provides a unified graph programming interface, enabling domain experts to promptly implement various types of matrix computations. The proposed paradigm leverages the underlying graph processing engine to achieve efficient execution, eliminating the needs for HPC expertise in programming large-scale scientific applications. We evaluate the performance of the developed graph compute engine for three typical scientific computing routines. Our results demonstrate that the graph engine-based scientific computing paradigm achieves performance comparable to the best-performing implementations based on existing parallel computing libraries and bespoke implementations. Importantly, the paradigm greatly simplifies the development of scientific computations on large-scale platforms, reducing the programming difficulty for scientists and facilitating b

关键词： Application programming interfaces (API)

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Optimizing data shuffling in data-parallel computation by understanding user-defined functions 9

Optimizing data shuffling in data-parallel computation by un...

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9th USENIX Symposium on Networked systems Design and Implementation, NSDI 2012

作者： Zhang, Jiaxing Zhou, Hucheng Chen, Rishan Fan, Xuepeng Guo, Zhenyu Lin, Haoxiang Li, Jack Y. Lin, Wei Zhou, Jingren Zhou, Lidong Microsoft Research Asia China Microsoft Bing United States Peking University China Services Computing Technologyand System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Georgia Institute of Technology United States

Map/Reduce style data-parallel computation is characterized by the extensive use of user-defined functions for data processing and relies on data-shuffling stages to prepare data partitions for parallel computation. Instead of treating user-defined functions as "black boxes", we propose to analyze those functions to turn them into "gray boxes" that expose opportunities to optimize data shuffling. We identify useful functional properties for userdefined functions, and propose SUDO, an optimization framework that reasons about data-partition properties, functional properties, and data shuffling. We have assessed this optimization opportunity on over 10,000 dataparallel programs used in production SCOPE clusters, and designed a framework that is incorporated it into the production system. Experiments with real SCOPE programs on real production data have shown that this optimization can save up to 47% in terms of disk and network I/O for shuffling, and up to 48% in terms of cross-pod network traffic. © 2012 by The USENIX Association. All Rights Reserved.

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APPLE: A novel P2P based e-learning environment

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3326卷 52-62页

作者： Jin, Hai Yin, Zuoning Yang, Xudong Wang, Fucheng Ma, Jie Wang, Hao Yin, Jiangpei Cluster and Grid Computing Lab. Huazhong University of Science and Technology Wuhan 430074 China

With the rapid development of information technology, dramatic changes have been taken place in the fundamental ways that people acquire and disseminate knowledge. Various e-Learning systems have been designed to help people take full advantage of the benefit brought by the technology. The newly emerged Peer to Peer (P2P) and grid computing will serve as the key driven forces to bring revolutionary impact on the future society. This paper evaluates the potential contribution of the P2P and grid technology into the e-Learning system and introduces a novel P2P based e-Learning environment, called APPLE. With the help of Gnutella-like P2P network, APPLE provides live broadcasting services to share services of education resources. We build a virtual classroom service based on ***, which exposes the service as a grid service that could be accessed by all the users among the grid. © Springer-Verlag Berlin Heidelberg 2004.

关键词： grid computing

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Honeycomb: A peer-to-peer substrate for on-demand media streaming service

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3358卷 330-339页

作者： Deng, Dafu Jin, Hai Zhang, Chao Chen, Hao Liao, Xiaofei Cluster and Grid Computing Lab. Huazhong University of Science and Technology Wuhan 430074 China

Peer-to-Peer media streaming service has gained tremendous momentum in recent years. However, a number of challenges in Peer-to-Peer media streaming have not been addressed. In this paper, we propose a peer-to-peer substrate for supplying on-demand media streaming service, called Honeycomb, which mainly addresses on the key technical issue of: constructing a locality-aware P2P overlay network with high scalab.lity and manageability. Honeycomb can fully utilize the locality of underlying physical network so that the bandwidth consumption used for the overlay maintenance can be effectively saved and the QoS requirements for delivering media content can be easily satisfied. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Media streaming

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Efficient immunization algorithm for peer-to-peer networks

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3296卷 232-241页

作者： Chen, Hao Jin, Hai Sun, Jianhua Han, Zongfen Cluster and Grid Computing Lab. Huazhong University of Science and Technology Wuhan 430074 China

In this paper, we present a detail study about the immunization of viruses in Peer-to-Peer networks exhibiting power-law degree distributions. By comparing two different immunization strategies (randomized and degree-based), we conclude that it is efficient to immunize the highly connected nodes in order to eradicate viruses from the network. Furthermore, we propose an efficient updating algorithm for global virus database according to the degree-based immunization strategy. © Springer-Verlag 2004.

关键词： Immunization

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xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction 23

xGCN: An Extreme Graph Convolutional Network for Large-scale...

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2023 World Wide Web Conference, WWW 2023

作者： Song, Xiran Lian, Jianxun Huang, Hong Luo, Zihan Zhou, Wei Lin, Xue Wu, Mingqi Li, Chaozhuo Xie, Xing Jin, Hai Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China Microsoft Gaming Redmond United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalab.lity, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalab.e. © 2023 ACM.

关键词： Graph neural networks

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PipeEdge: A Trusted Pipelining Collab.rative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collab.rate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collab.rate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collab.rative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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A Semantic Double-Buffer Based Approach to Enhance Semantic Web Search

A Semantic Double-Buffer Based Approach to Enhance Semantic ...

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International Conference on the Digital Society

作者： Ling Chen Hai Jin Sheng Di Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

With the development of semantic Web, search efficiency is becoming a challenging issue. To address this problem, we design an approach which mainly has two features. First, we explore potential semantic relationship between different objects and make full use of them to build a semantic buffer on server end, enhancing search speed. Second, we put another relatively small semantic buffer on each client so as to adapt to each individual user's interests, further improving the whole search efficiency and reducing server's load. Through testing on a practical platform, we testified that this semantic double-buffer based search approach can not only effectively reduce user's request response time, but considerably release server's load, bringing a high scalab.lity.

关键词： Semantic Web Testing Resource description framework Algorithm design and analysis Indexing grid computing Computers Delay Scalab.lity Information retrieval

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Building dynamic integrity protection for multiple independent authorities in virtualization-based infrastructure

Building dynamic integrity protection for multiple independe...

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2009 10th IEEE/ACM International Conference on grid computing, grid 2009

作者： Cheng, Ge Jin, Hai Zou, Deqing Zhang, Xinwen Li, Min Yu, Chen Xiang, Guofu Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Samsung Information Systems America San Jose CA United States

ISBN: (纸本)9781424451494

In grid and cloud computing infrastructures, the integrity of a computing platform is a critical security requirement in order to provide secure and honest computing environments to service providers and resource consumers. However, due to the fact that software components running on a single platform are usually provided and maintained by different authorities which are potentially untrusted to each other, the problem to monitor and protect runtime system integrity become very challenging and has not been well addressed yet. In this paper, we present a virtualization based dynamic integrity protection method which ensures that only appropriate authorities can control over their components without interfering with other component providers or authorities. In our solution, integrity requirements defined by the authorities of upper components (e.g., service middleware and applications) are respected by preventing the underlying components (e.g., operating system) from exposing their sensitive data, which can be caused by update of the underlying components or other malicious actions. We implement our solution on Xen-based platform, and our evaluation results show that the solution is effective for integrity protection with acceptable performance overhead. © 2009 IEEE.

关键词： Virtualization

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